In a vehicle of today, in order to ensure safe traveling or to support a driving operation of a driver, a condition around the vehicle is monitored using various techniques. As a representative one of these techniques, there is known a technique that analyzes a captured image obtained from an vehicular camera to detect a pedestrian in the captured image.
In the detection of a pedestrian in a captured image, a feature of the pedestrian to be detected is stored with mainly focusing on the outer shape of the pedestrian, and a search for an area having the feature of the pedestrian in the captured image is performed. When the area having the feature of the pedestrian is found, the area is determined to include the pedestrian (Patent Literature 1, for example).
However, the pedestrian is not solely included in the captured image. Thus, when the search for an area having the feature of the pedestrian is performed in the captured image, an area having the feature of the pedestrian as a whole is searched from an image that also includes the surroundings of the pedestrian.
Further, a condition around the pedestrian in the captured image varies according to environmental factors during image capturing. For example, when an image is captured in good weather, a shadow of the pedestrian is also included together with the pedestrian. Further, the length of the shadow and contrast with the background vary according to seasons or time of image capturing. Further, since clothes change according to seasons or areas, the outer shape itself of the pedestrian also changes.
Thus, in the search for a pedestrian in a captured image, when an area does not completely have the feature of the pedestrian, but roughly has the feature of the pedestrian, the area is determined to include the pedestrian.
However, in the above conventional technique, there is a limit in improving the pedestrian detection accuracy because of the following reason. In order to improve the pedestrian detection accuracy, it is first necessary to improve the pedestrian detectivity (the ratio of the number of detected pedestrians to the total number of pedestrians included in the captured image) and a percentage of correct detections (the ratio of images that actually include pedestrians) at the same time.
Of course, when a pedestrian cannot be detected due to changes in environmental factors, it is not possible to improve the detectivity. When the determination criterion to determine whether an image includes the feature of the pedestrian is loosened so as to be able to detect a pedestrian even when the environmental factors change, an area that can be seen as having a shape close to a pedestrian by chance in the captured image is incorrectly detected as a pedestrian. Thus, the percentage of correct detections is reduced. On the contrary, when the determination criterion to determine whether an image includes the feature of a pedestrian is made strict to avoid a reduction in the percentage of correct detections caused by incorrect detection, it becomes difficult to detect a pedestrian due to the influence by changes in environmental factors. Thus, the detectivity is reduced. In this manner, after the pedestrian detection accuracy is improved to some extent, there is an antinomy relationship between the pedestrian detectivity and the percentage of correct detections. Thus, it is difficult to further improve the pedestrian detection accuracy.